System and method of assessment of the efficacy of treatment of neurological disorders using the electroencephalogram

ABSTRACT

Disclosed is a system and method of assessing the efficacy of treatment of neurological or psychological disorders. The preferred embodiment uses at least two surface electrodes to acquire EEG signals from the surface of a patient&#39;s body, a processor for computing from the EEG signals various features and indices that are representative of the patient&#39;s neurological or psychological state. Changes in these parameters may be used to assess the efficacy of treatment and to modify the treatment to optimize the resultant patient state.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 60/468,350 filed on May 6, 2003 and U.S. Provisional ApplicationSer. No. 60/534,247 filed on Jan. 5, 2004.

BACKGROUND OF THE INVENTION

There are a wide range of neurological and psychological disorders forwhich treatment may be provided by various means. For many disorders,administration of pharmaceutical agents is the most common treatmentmodality. In cases in which the symptoms of the disorder are resistantto pharmacological treatment or for which no pharmacological treatmentexists, other modalities may be used, including neurostimulation.

Neurostimulation is a method of disease treatment which uses anelectrical stimulator to provide a current signal which is used tostimulate the central nervous system (CNS), generally either directly orby means of a nerve of the peripheral nervous system. Suchneurostimulators and their corresponding electrodes are generallyimplanted in a patient's body. There are currently two primary methodsof neurostimulation for central nervous system disorders; deep brainstimulation (DBS) and vagus nerve stimulation (VNS). DBS uses anelectrode implanted directly in a patient's brain, while VNS stimulatesa patient's vagus nerve peripherally.

A commercially available DBS neurostimulator is manufactured and sold byMedtronic Inc. of Minneapolis, Minn., USA, model 3386, having astimulating lead with four cylindrical stimulating electrodes. The deepbrain stimulator is a surgically implanted medical device, similar to acardiac pacemaker, which delivers high-frequency, pulsatile electricalstimulation to precisely targeted areas within the brain. The deviceconsists of a very small electrode array (electrodes 1.5 mm in lengthwith 3 mm center to center separation) placed in a deep brain structureand connected through an extension wire to an electrical pulse generatorsurgically implanted under the skin near the collarbone. The MedtronicDBS has received marketing clearance from the US Food and DrugAdministration (FDA) with an indication for treatment of Parkinson'sDisease, Essential Tremor, and Dystonia. Current research is evaluatingDBS as a treatment for epilepsy, psychiatric disorders, and chronicpain.

The DBS stimulator is surgically placed under the skin of the chest ofthe patient. The stimulating DBS electrode lead is connected to the DBSstimulator wires and is placed in a specific inter-cranial locationwhich may vary depending on the region of the brain being treated. TheDBS system is adjusted by several parameters: 1. location of the 4electrode lead, 2. selection of the stimulating electrodes, 3. amplitudeof the stimulator signal, 4. frequency (repetition rate) of thestimulator signal, 5. polarity of the stimulating signal, and 6. pulsewidth of the stimulating signal. Post-implantation, all of theseparameters except electrode location can be non-invasively varied by aclinician to enhance therapeutic effectiveness and minimize sideeffects. Amplitude, measured in volts, is the intensity or strength ofthe stimulation. The typical range is 1.5 to 9 volts. Frequency is therepetition rate at which the stimulation pulse is delivered and ismeasured in pulses per second (Hz); it typically ranges from 100-185 Hz.The pulse width is the duration of the stimulation pulse, measured inmicroseconds. The average pulse width ranges from 60-120 microseconds.

Another commercially available neurostimulator is designed for use onthe peripheral nervous system, specifically the vagus nerve. An exampleof this type of system is designed and sold by Cyberonics Corporation.The Vagus Nerve Stimulator (VNS) Therapy device is implanted in apatient's chest under the skin immediately below the collarbone or closeto the armpit. Two tiny wires from the device wrap around the vagusnerve on the left side of the neck. Through stimulation of thisperipheral nerve, brain function is affected. VNS therapy has beengranted marketing clearance by the FDA with an indication for treatmentof epilepsy and is being investigated to treat a number of other centralnervous system diseases and conditions, such as depression, obesity,Alzheimer's disease, etc.

An obstacle to the broader use of these devices is, in many indications,the lack of a measure of treatment efficacy. The efficacy ofneurostimulation is a function of the settings of the various stimulatorparameters (i.e., electrode selection, stimulus pulse amplitude,stimulus pulse frequency, stimulus polarity and stimulus pulse width,among others). However, with the exception of treatment for essentialtremor or patients with very frequent epileptic seizures, it isdifficult to assess the effect of the stimulus provided, and thusdifficult to adjust these parameters to achieve the maximum possibletreatment efficacy.

Prior Art

A number of different approaches have used the EEG as a feedback signalfor neurostimulation.

In U.S. Pat. No. 6,263,237 issued to Rise, the use of a sensor incombination with a signal generator (neurostimulator) to treat ananxiety disorder is described. In this embodiment, the sensor generatesa signal related to a condition resulting from the anxiety disorder.Control means responsive to the sensor signal regulate the signalgenerator so that the neurological disorder is treated. One of the typesof sensor signals is cortical potentials recorded above the neuronscontrolling specific aspects of behavior associated with theneurological disorder; in this case, the sensor would take the form ofan implanted depth electrode. In this system, the sensor is an integralcomponent of the stimulating device. There is no teaching or suggestionin the patent, however, of the method of obtaining or computing a sensorsignal relating to the anxiety disorder or to treatment efficacy.

In U.S. Pat. No. 6,066,163 issued to John, an Adaptive Brain Stimulation(ABS) system which aids in the rehabilitation of patients from traumaticbrain injury, coma, or other brain dysfunction is described. The systemcomprises a sensor(s), a stimulating means, a comparator means forstatistical comparison, and a means to adjust the stimulator accordingto the outcome of the comparison. The object of the system is to improvetreatment of central nervous system pathology such as coma by relying onstatistically significant and medically meaningful criteria to choose aspecified program of stimulation. The John system specifically utilizessignals from the brain (EP and EEG), as well as EKG and EMG. Johndescribes a large number of potential parameters that may be computedfrom these signals. The parameters are compared using statisticalmethods to a set of reference values from a database which may includevalues previously obtained from the patient, values that medicalpersonnel have obtained, or values from an appropriate normativepopulation. The ABS then selects a set of stimulation parameters basedupon this comparison. A positive outcome is defined as the current statemeeting a set of criteria indicating an improvement in the patient'scondition. John describes the method only in a general sense; the patentdoes not teach any specific method or the use of any specific signals orparameters to quantify those signals, nor does it teach criteria whichdefine positive outcomes. In addition, John does not teach the making ofan index of treatment efficacy.

U.S. Pat. No. 6,539,263 issued to Schiff et al. describes a system fortreating a conscious patient to improve cognitive function orcoordination of function across a patient's cortical regions. Electricalstimulation is applied to at least a portion of the subcorticalstructures involved in the generation and control of generalizedefference copy signals under conditions effective to improve thepatient's cognitive function. Internally generated movement of thepatient is then detected and in response to such internally generatedmovement, application of electrical stimulation is controlled. Schiff,et al. also state that their method can be optimized by monitoringregional and intrahemispheric changes in brain waves as measured byconventional techniques (EEG or magnetoencephalogram (MEG)) or bymonitoring regional and intrahemispheric changes in metabolic activity.Schiff, et al., however, do not teach specific methods for processingthe EEG or MEG signal to produce a parameter reflective of cognitivefunction.

U.S. Published Patent Application 2002/0013612A, filed by Whitehurst,describes a system for applying drugs and/or applying electricalstimulation to the brain to treat mood and/or anxiety disorders. Thesystem described is fully implanted in the skull. In order to helpdetermine the strength and/or duration of electrical stimulation and/orthe amount and/or type(s) of stimulating drug(s) required to produce thedesired effect, in one preferred embodiment, a patient's response toand/or need for treatment is sensed. Whitehurst states that the methodsof determining the required electrical and/or drug stimulation includemeasuring the electrical activity of a neural population (e.g., EEG),measuring neurotransmitter levels and/or their associated breakdownproduct levels, measuring medication and/or other drug levels, hormonelevels, and/or levels of any other bloodborne substance(s). He furtherstates that the sensed information is preferably used to control thestimulation parameters of the System Control Unit(s) in a closed-loopmanner. Whitehurst does not teach any method of processing the EEGsignal to produce a parameter that can be used as a control variable,nor does he teach recording EEG from the surface of the head.

Others have examined EEG asymmetries (i.e., differences EEG metricsbetween brain hemispheres); “The common observation inelectroencephalographic (EEG) studies of an altered pattern ofasymmetric activation in anterior scalp regions in the reduced leftrelative to right activation in depressed or dysphoric individuals . . .”.

A principal object of the present invention is to derive clinicallymeaningful information from the electroencephalogram signal to helpoptimize neurostimulation therapy.

SUMMARY OF THE INVENTION

The present invention describes a system and method for assessing theefficacy of treatment for neurological or psychological conditions.Treatment efficacy is assessed by interpretation of changes in the EEGsignal. It is well known that neurostimulation of the thalamus caninfluence the EEG. This invention is based on the concept thatexcitation or inhibition of brain circuits is manifested in specific EEGchanges that can be characterized by and associated with the efficacy ofDeep Brain Stimulation or Vagus Nerve Stimulation treatment.

The invention described in this application enables the quantificationand monitoring of the efficacy of various methods of treatment ofneurological and psychological disorders. In the preferred embodimentthe efficacy of neurostimulation of the peripheral and/or centralnervous system is quantified. Examples of diseases and conditions towhich the invention may be applied include depression, obsessivecompulsive disorder, epilepsy, Parkinson's disease, movement disorders,and stroke. Similarly, while the preferred embodiment describes thequantification of the efficacy of neurostimulation, this invention maybe used to monitor the efficacy of other types of treatment as well,including but not restricted to pharmacological treatment,electroconvulsive therapy (ECT) and transcranial magnetic simulation(TMS).

In the case of inhibition of brain function via deep brain or vagusnerve stimulation, a disruption of a cortex to deep-brainneuro-transmission signal path may occur. This would result in adecrease in EEG signal power. Conversely, if the neurostimulationactivates or enhances a neuro-transmission pathway, an increase in EEGsignal power may occur. Observations of DBS patients indicate that theneurostimulation used currently to treat patients suffering fromobsessive-compulsive disorder and depression by bilaterally stimulatingthe anterior limb of the internal capsule (an anatomical region of thebrain near the thalamus) causes a reduction in frontal EEG powerreferenced to the left earlobe and the right earlobe, specifically inthe alpha (8-12 Hz) and/or theta (4-8 Hz) frequency bands. This decreasein power is consistent with the hypothesis that frontal alpha power isgenerated by a cortex-to-thalamus neuro-pathway and that the DBSinterferes with that pathway.

The invention described herein processes the EEG signals that aredirectly or indirectly affected by the area of the brain that is beingstimulated. An index of neurostimulation treatment efficacy is generatedfrom the EEG signal using spectral and/or time-domain features. Askilled clinician then adjusts the neurostimulator settings or locationbased on the EEG changes. The preferred embodiment uses EEG measuredfrom two EEG channels, left earlobe (A₁) referenced to the foreheadmidline (Fp_(Z)) and right earlobe (A₂) referenced to Fp_(Z) incombination. The two EEG signals are then used to calculate a numericalindex which is reflective of the efficacy of neurostimulator treatment.This methodology can be extended to apply to other EEG parameters(including those that are time-based as well as frequency-based)obtained from other electrode locations and other modes of treatment ofthe brain including both device and pharmacological treatments.

These and other features and objects of the present invention will bemore fully understood from the following detailed description whichshould be read in light of the accompanying drawings in whichcorresponding reference numerals refer to corresponding parts throughoutthe several views.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the system of the present invention.

FIG. 2 is a flow chart of a method of computation of the power spectraland auto/cross bispectral arrays of the present invention.

FIG. 3 is a flow chart of an alternate method of computation of thepower spectral and auto/cross bispectral arrays of the presentinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The invention described herein is a method of assessing the efficacy oftreatment of neurological and psychiatric disorders by assessing changesin neuronal activity as manifested in the EEG. A particular embodimentof the invention involves a system for assessing the effect of theelectrical stimulation provided by a neurostimulator 60 connected to apatient 10 via a stimulating electrode lead 70 (FIG. 1). The systemincorporates a Data Acquisition Unit (DAU) 20 used to acquire asubject's EEG signal for subsequent processing. The DAU 20 typicallyconsists of a computer system with an integral analog-to-digital (A-D)converter 25 and a set of electrodes 15 that are placed on the scalp ofa subject 10. The A-D converter is used to transform analog EEG signalsobtained from a set of surface electrodes into a sampled set of signalvalues that may then be analyzed by the computer of the Data ComputationUnit (DCU) 30. The DCU 30 incorporates a processor 35 and acommunications device 36 that receives the sampled values from the DAU20. In this embodiment, the processors of the DAU 20 and DCU 30 are oneand the same. In alternate embodiments, however, the DAU 20 may acquirethe EEG signals and transmit the sampled EEG signals over acommunications link to a remote DCU 30. Such a communications link maybe a serial or parallel data line, a local or wide area network, atelephone line, the Internet, or a wireless connection. The clinicianconducting the assessment may communicate with the DCU 30 using akeyboard 40 and display device 50.

EEG data is acquired from the surface of a patient's body using surfaceelectrodes 15. When the electrodes are all to be placed below thehairline, the electrodes are preferably of the Zipprep® typemanufactured by Aspect Medical Systems, Inc. (Newton, Mass.). Whenelectrodes are placed within the hair, gold-cup type electrodes may beused, held in place by either collodion or a physical restraint. Avariety of different electrode placements, or montages, may be used. Thepreferred embodiment uses an electrode arrangement (montage) of the leftearlobe (A₁) referenced to the center of the forehead (Fp_(Z)) and theright earlobe (A₂) referenced to Fp_(Z) in combination, in which a firstchannel of EEG signal is the voltage observed between electrodelocations A₁ and Fp_(Z) (A₁-Fp_(Z)) and a second channel of EEG is thevoltage observed between electrode locations A₂ and Fp_(Z) (A₂-Fp_(Z)).An alternate embodiment uses an electrode montage in which the firstchannel is the voltage between electrode locations F₇-Fp_(Z) and asecond channel of EEG is the voltage observed between electrodelocations F₈-Fp_(Z). Another alternate embodiment uses the BIS Sensor(Aspect Medical Systems Inc.), which uses the unilateral montage ofFp_(z)-At1, Fp_(z)-SM94 ₁, where At1 is on the left temple lateral tothe eye (0.75 inches anterior to the malar bone) and SM94 ₁ is 2.5inches lateral to Fp_(z). This montage is described as being on the leftside of the head, but may equivalently be on the right side, in whichcase it is denoted as Fp_(z)-At2, Fp_(z)-SM94 ₂. Alternatively, anyconfiguration of electrode locations may be used, such as thosedescribed by the International 10/20 Electrode Montage System describedby HH Jasper in “The Ten-Twenty Electrode System of the InternationalFederation in Electroencephalography and Clinical Neurology”, The EEGJournal, 1958; 10 (Appendix), pp. 371-5., using both referential andunipolar configurations.

EEG signals acquired by the electrodes 15 are sampled by the D/Aconverter 25 of the DAU 20 to create a sampled data set, preferably at asampling rate of 128 samples/second. The sampled data set is divided foranalysis purposes in the preferred embodiment into 2 second (256 sample)records (epochs). After the DCU 30 receives the sampled values from theDAU 20, the DCU 30 first examines the sampled EEG signals for artifactarising from patient movement, eye blinks, electrical noise, etc.Detected artifact is either removed from the signal, or the portion ofthe signal with artifact is excluded from further processing. High-passfiltering is also employed to reduce the tendency of power atfrequencies above the signal band of interest from appearing at lowerfrequencies due to an inadequate sampling frequency (aliasing).

The DCU 30 next computes a set of parameters from the artifact-free EEGdata. Such parameters may include power spectral arrays, bispectralarrays, higher-order spectral arrays (trispectrum, etc.), cordance (suchas described in U.S. Pat. Nos. 5,269,315 and 5,309,923), z-transformedvariables, entropy parameters, and time-domain parameters, including butnot limited to template matching, peak detection, threshold crossing,zero crossings and Hjorth descriptors. Such parameters, spectral orotherwise, which quantify some aspect of the data are referred to asfeatures. The DCU 30 calculates from the parameters a series of featuresand indices that are indicative of the subject's severity ofneurological dysfunction or level of neurological condition. Byobserving how these features and indices change in response to theneurostimulation provided by the neurostimulator 60, the stimulationparameters may be varied to modulate the neurostimulation effect. Thesefeatures and indices may be displayed to the user on the display device50. In the embodiment in which the DCU 30 is remote from the DAU 20, theresult may be transmitted back to a display device on the DAU 20, ortransmitted to the patient's physician via e-mail or made available viaa secure web page.

Calculation of the Spectral Arrays

In the preferred embodiment, the features of the index are calculatedfrom spectral arrays, defined as any of the power spectral arrays,bispectral arrays or higher-order spectral arrays (trispectrum, etc.),The power spectral and bispectral data arrays may be calculated usingfrequency domain (Fourier transform) methods as well as time domain(autoregressive) methods. The term power spectral arrays or powerspectrum includes any or all of the power spectral, cross spectral andcoherence arrays. The term bispectral arrays or bispectrum includes allor any of the following arrays, for both auto and cross formulations:complex triple product, real triple product, bispectral density, biphaseand bicoherence arrays. The power spectral arrays are calculated as anintermediate step of the bispectral array computation and are thusavailable for the derivation of parameters to be used as features in anindex. In the case in which only power spectral arrays are used tocalculate an index, the computation may be ended after the needed arraysare computed. Both frequency and time domain methods will be illustratedhere, and those skilled in the art will recognize that other methods maypotentially be derived, as well. The invention is intended toincorporate all computational methods of obtaining the power spectraland bispectral arrays.

Referring now to FIG. 2, the frequency domain-based procedures forproducing the power spectral, cross-spectral, coherence, autobispectralor the cross-bispectral arrays will now be discussed. In step 802, thesystem checks whether the computation to be performed is an autospectralor cross-spectral computation. Autobispectral analysis is a special caseof cross-bispectral analysis and therefore different rules of symmetryapply.

In step 804, the system sets the following symmetries in order toproceed with autobispectral computation:f ₁ +f ₂ ≦f _(s)/20≦f ₂ ≦f ₁where f_(s) is the sampling rate (128 samples/second in the preferredembodiment which uses 128 2-second records, resulting in a frequencyresolution of 0.5 Hz), and f₁ and f₂ (also referred to as Frequency 1and Frequency 2) denote the frequency pairs over which cross-spectral orbispectral computation will be carried out. In addition, for the powerspectral and autobispectral computation,X _(i)(t)=Y _(i)(t)→X _(i)(f)=Y _(i)(f)X_(i)(t) and Y_(i)(t) denote the individual time series records used forpower and bispectral computation. In the preferred embodiment, X_(i)(t)and Y_(i)(t) are sampled EEG records obtained simultaneously fromdifferent channels. They may also be successive records from the samechannel. X_(i)(f) and Y_(i)(f) denote the Fourier transforms of the timeseries records X_(i)(t) and Y_(i)(t), respectively, and i denotes therecord number.

In step 806, the following symmetries are adhered to forcross-bispectral analysis:f ₁ +f ₂ ≦f _(s)/20≦f ₁ ≦f _(s)/20≦f ₂ ≦f _(s)/2X _(i)(t)≠Y _(i)(t)→X _(i)(f)≠Y _(i)(f)where all variables represent the same values as they do forautobispectral analysis, except that for cross-spectral analysisX_(i)(t) and Y_(i)(t) represent individually derived time seriesrecords.

The fast Fourier transform (FFT) X_(i)(f) and Y_(i)(f) of the selectedrecords is computed using a standard IEEE library routine or any otherpublicly available routine in step 808.

In Step 810, the power spectra P_(Xi)(f) and P_(Yi)(f) of each of theselected records is computed by squaring the magnitudes of each elementof the Fourier transforms X_(i)(f) and Y_(i)(f), respectively.P _(Xi)(f)=|X _(i)(f)|²P _(Yi)(f)=|Y _(i)(f)|²The cross spectral array P_(XY)(f) and the coherence array γ_(XY) ²(f)may also be calculated as:

$\quad\begin{matrix}{{P_{{XY}_{i}}(f)} = {{X_{i}^{*}(f)}\;{Y_{i}(f)}}} \\{{P_{XY}(f)} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{P_{{XY}_{i}}(f)}}}} \\{{P_{X}(f)} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{P_{X_{i}}(f)}}}} \\{{P_{Y}(f)} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{P_{Y_{i}}(f)}}}} \\{{\gamma_{XY}^{2}(f)} = \frac{{{P_{XY}(f)}}^{2}}{{P_{X}(f)}\;{P_{y}(f)}}}\end{matrix}$where X_(i)*(f) is the complex conjugate of X_(i)(f) and M is the numberof records (128 in the preferred embodiment).

The system computes the average complex triple product in step 812 byutilizing the following equations where bc_(i)(f₁,f₂) is the individualcomplex triple product from one record and BC(f₁,f₂) is the averagecomplex triple product:bc _(i)(f ₁ ,f ₂)=X _(i)(f ₁)Y _(i)(f ₂)Y _(i)*(f ₁ +f ₂)where Y_(i)*(f₁+f₂) is the complex conjugate of Y_(i)(f₁+f₂), and

${{BC}\left( {f_{1},f_{2}} \right)} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{{bc}_{i}\left( {f_{1},f_{2}} \right)}}}$

The average real triple product is computed in step 814 by using thefollowing equations where P_(Xi)(f) and P_(Yi)(f) are the power spectrafrom one record, br_(i)(f₁,f₂) is an individual real triple product fromone record and BR(f₁,f₂) is the average real triple product:br _(i)(f ₁ ,f ₂)=P _(Xi)(f ₁)P _(Yi)(f ₂)P _(Yi)(f ₁ +f ₂)

${{BR}\left( {f_{1},f_{2}} \right)} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{{br}_{i}\left( {f_{1},f_{2}} \right)}}}$Note that P_(Yi) is real valued, and therefore P_(Y)=P_(Yi)*.

In step 816, the bispectral density array BD(f₁,f₂) is computed usingthe following equation:BD(f ₁ ,f ₂)=|BC(f ₁ ,f ₂)|

In step 818, the system computes the biphase array φ(f₁,f₂) using thefollowing equation:

${\phi\left( {f_{1},f_{2}} \right)} = {\tan^{- 1}\left( \frac{{Im}\left( {{BC}\left( {f_{1},f_{2}} \right)} \right)}{{Re}\left( {{BC}\left( {f_{1},f_{2}} \right)} \right)} \right)}$0≦φ≦2π (radians)

In step 820, the system computes the bicoherence array R(f₁,f₂) usingthe following equation:

${R\left( {f_{1},f_{2}} \right)} = \frac{{BD}\left( {f_{1},f_{2}} \right)}{\sqrt{{BR}\left( {f_{1},f_{2}} \right)}}$0≦R≦1

In step 822, the system returns the requested auto/cross bispectralarrays to the Data Computation Unit 30.

Now turning to FIG. 3, a parametric based method for calculating theauto/cross bispectral arrays will now be described. In steps 902, 904,and 906 the system sets the symmetries and time series records in thesame manner as described above in steps 802, 804, and 806 respectively.The power spectra of X_(i)(t) and Y_(i)(t) are estimated in steps 908,910, and 912. In addition, the cross spectral and coherence arrays arecomputed. This estimation method includes two major stages, theautoregressive (AR) model order selection and the power spectrumcomputation for X_(i)(t) and Y_(i)(t). In step 908, the system computestwo sequences of autocorrelations, {R_(2X)(m)} and {R_(2Y)(m)} using thefollowing equation.

${{R_{2z}(m)} = {\frac{1}{M*N}{\sum\limits_{i = 1}^{M}\;{\sum\limits_{t = 0}^{N - {m}}\;{{z_{i}(t)}{z_{i}\left( {t + m} \right)}}}}}}\mspace{50mu}$z=X, Y, and m=0, 1, . . . , L

where M is the number of records and N is the number of samples perrecord (128 and 256, respectively, in the preferred embodiment), and Lis much greater than the possible AR filter order (L=50 in the preferredembodiment). The Final Prediction Errors, FPE_(X)(m) and FPE_(Y)(m) arecalculated for all orders, m=0, 1, 2, . . . L, by performing a Levinsonrecursion function on each autocorrelation sequence in step 910 in orderto find the order of the AR filter. The locations of the minima ofFPE_(X)(m) and FPE_(Y)(m), Q_(X) and Q_(Y), respectively, are chosen tobe the orders of the AR filters of power spectra of X_(i)(t) andY_(i)(t) respectively, i.e.,FPE _(X)(Q _(X))=min{FPE _(X)(m)}FPE _(Y)(Q _(Y))=min{FPE _(Y)(m)}

Once the orders of the AR filters for power spectra are chosen, theautocorrelation sequences, {R_(2X)(m)} and {R_(2Y)(m)}, are entered intoLevinson recursion with orders Q_(X) and Q_(Y), respectively, instead ofL. The coefficients, {c_(iX), i=0, 1, . . . , Q_(X)} and {c_(iY), i=0,1, . . ., Q_(Y)}, obtained from the recursion are the coefficients ofthe AR filters for the power spectra of X_(i)(t) and Y_(i)(t),respectively. Then, in step 912, the power spectra P_(X)(f) and P_(Y)(f)are computed as the prediction error (σ_(z) ²) divided by square of themagnitude of the Fourier transform of the coefficients, i.e.,

${P_{z}(f)} = \frac{\sigma_{z}^{2}}{{{1 + {\sum\limits_{i = 1}^{Q_{z}}{c_{iz}{\mathbb{e}}^{{- {j2\pi}}\;{fi}}}}}}^{2}}$z=X, Y

Similarly, the cross spectra P_(xy)(f) can be calculated as

${P_{XY}(f)} = \frac{\sigma_{X}\sigma_{Y}}{{{1 + {\sum\limits_{i = 1}^{Q_{X}}{c_{iX}{\mathbb{e}}^{{- {j2\pi}}\;{fi}}}}}}{{1 + {\sum\limits_{i = 1}^{Q_{Y}}{c_{iY}{\mathbb{e}}^{{- {j2}}\;\pi\;{fi}}}}}}}$and the coherence array is calculated from P_(x)(f), P_(Y)(f) andP_(xy)(f) as above.

The system estimates the auto/cross real and complex triple products insteps 914, 916, and 918. The estimation process includes two majorstages: the order selection and real and complex triple productcomputation. In step 914, two sequences of third-order moments,{R_(3X)(τ)} and {R_(3Y)(τ)} are computed using the following equation.

${R_{3z}(\tau)} = {\frac{1}{M*N}{\sum\limits_{i = 1}^{M}{\sum\limits_{t = s_{1}}^{s_{2}}{{z_{i}(t)}{z_{i}^{2}\left( {t + \tau} \right)}}}}}$z=X, Y, and τ=−L, . . . , L

where s₁=max (1, 1−τ), s₂=min (N, N−τ), and L is much greater than thepossible AR filter orders (e.g. 50).

In step 916, two super matrices T_(X) and T_(Y) are formed as follows.

$T_{z} = \begin{pmatrix}{R_{3z}\left( {- L} \right)} & {R_{3z}\left( {{- L} + 1} \right)} & \cdots & {R_{3z}(0)} \\{R_{3z}\left( {{- L} - 1} \right)} & {R_{3z}\left( {- L} \right)} & \cdots & {R_{3z}\left( {- 1} \right)} \\\vdots & \vdots & \vdots & \vdots \\{R_{3z}\left( {{- 2}L} \right)} & {R_{3z}\left( {{{- 2}L} + 1} \right)} & \cdots & {R_{3z}\left( {- L} \right)}\end{pmatrix}$z=X, Y

From the assumption we made about the AR filter of the bispectralarrays, the orders O_(X) and O_(Y) of the AR filters of the bispectralarrays of X_(i)(t) and Y_(i)(t) are the ranks of the super matricesT_(X) and T_(Y). Therefore, O_(X) and O_(Y) are chosen by using singularvalue decomposition. Having found the orders, we obtain the coefficientsof the AR filters of the bispectral arrays by solving the followinglinear system of equations:

${\begin{pmatrix}{R_{3z}(0)} & {R_{3z}(1)} & \cdots & {R_{3z}\left( O_{z} \right)} \\{R_{3z}\left( {- 1} \right)} & {R_{3z}(0)} & \cdots & {R_{3z}\left( {O_{z} - 1} \right)} \\\vdots & \vdots & \vdots & \vdots \\{R_{3z}\left( {- O_{z}} \right)} & {R_{3z}\left( {{- O_{z}} + 1} \right)} & \cdots & {R_{3z}(0)}\end{pmatrix}\begin{pmatrix}1 \\b_{1z} \\\vdots \\b_{O_{z}z}\end{pmatrix}} = \begin{pmatrix}\beta_{z} \\0 \\\vdots \\0\end{pmatrix}$z=X, Y

where the skewness (β_(z)) and the coefficients (b_(1z), . . . ,b_(Oz)z), z=X, Y, can be obtained by solving the linear system ofequations.

The average auto/cross complex triple product of X_(i)(t) and Y_(i)(t)are computed in step 918 as the cubic root of the triple product of theskewnesses, (β_(X) β_(Y) β_(Y))^(1/3), divided by the triple product ofthe Fourier transforms of the AR filter coefficients (H_(z)(f)), i.e.,BC(f ₁ ,f ₂)=(β_(X)β_(Y)β_(Y))^(1/3)/(H _(X)(f ₁)H _(Y)(f ₂)H _(Y)*(f ₁+f ₂))

${H_{z}(f)} = {1 + {\sum\limits_{i = 1}^{O_{z}}{b_{iz}{\mathbb{e}}^{{- j}\; 2\;\pi\;{fi}}}}}$z=X, Y

and BR(f₁,f₂) is the average auto/cross real triple product:BR(f ₁ ,f ₂)=P _(X)(f ₁)P _(Y)(f ₂)P _(Y)(f ₁ +f ₂)

After obtaining the average auto/cross complex and real triple products,the system computes the bispectral density, biphase, and bicoherencearrays in step 920 the same way as in steps 816, 818, 820. In step 922,the system returns the requested bispectral arrays to the DataComputation Unit 30.

Calculation of an Index of Neurostimulation Efficacy

An index may be constructed using features calculated from the spectralarrays as well as by means of other frequency and time domain methods.In the preferred embodiment, such an index is designed to quantify EEGchanges related to neurostimulator treatment efficacy. Development ofsuch an index requires a data set of EEG data from individuals with thespecified pathological condition the neurostimulator is intended totreat, along with the neurostimulator status before and during therecording and an independent measure of treatment status and efficacy.

In the development of the present embodiment, EEG data was recorded froma series of patients with major depressive disorder (MDD) orobsessive-compulsive disorder (OCD) with implanted DBS stimulators. EEGrecordings were made while patients were awake with their eyes closed.EEG data was recorded from electrode pairs A₁-Fp_(Z) (left hemisphere)and A₂-Fp_(Z) (right hemisphere) prior to DBS stimulation (the baselinerecording) and subsequently during multiple on-off stimulator cycles. Atthe time of each recording, the subjects self-reported their mood on ascale from 1-10 (i.e., 1 and 10 being the worst and best moodsimaginable) as well as their level of anxiety (1 being not anxious atall, 10 being the most anxious imaginable). The mood and anxiety scoresare measures of patient status that are independent of the EEG, and thechange in mood with treatment (here, neurostimulation) is an independentmeasure of treatment efficacy. To increase the dynamic range of the moodassessments, EEGs were recorded with the stimulator both off (typicallyresulting in poorer mood) and on (typically resulting in improved mood).For each of the channels A₁-Fp_(Z) and A₂-Fp_(Z), the various spectralarrays were calculated as described above, a separate array beingcalculated for the time period immediately preceding each of thepatient's assessments of mood and anxiety. Average EEG spectral arrayswere calculated for all frequencies at 0.5 Hz resolution using 2-secrecords of the first 30 seconds of artifact-free EEG.

In the preferred embodiment, a feature was constructed as the absolutepower within the alpha frequency range (8-12 Hz) averaged over 2 EEGchannels (A1-Fpz and A2-Fpz). This feature, the Absolute Alpha Power, iscalculated as

${{Absolute\_ Alpha}{\_ Power}} = \frac{\left( {{\sum\limits_{f = 8}^{12}{P(f)}_{A1\_ FPz}} + {\sum\limits_{f = 8}^{12}{P(f)}_{A2\_ FPz}}} \right)}{2}$

The absolute power is summed in the alpha frequency region separatelyfor each EEG channel, and the average alpha power is calculated over the2 channels. The correlation of Absolute Alpha Power with mood score issystematically negative, so that alpha power decreases as subjects' moodscores increase. The Pearson linear correlation between absolute alphapower and mood score is statistically significant (R=−0.821, p=0.012).

Although the preferred embodiment uses two channels of EEG data,alternate embodiments may include data from one or a plurality ofchannels. In addition, biological systems vary to some degree, sosomewhat different frequency ranges are likely to provide equivalentperformance. Similarly, other frequency ranges may be used.

Another feature calculated from the power spectral array in thepreferred embodiment is the difference in absolute power in the alphafrequency range (8 Hz≦f≦12 Hz) between the left and right hemispheres.This feature, the Absolute Alpha Asymmetry, or interhemisphericdifference, is calculated as

${{Absolute\_ Alpha}{\_ Asymmetry}} = {{\sum\limits_{f = 8}^{12}{P(f)}_{A1\_ FPz}} - {\sum\limits_{f = 8}^{12}{P(f)}_{A2\_ FPz}}}$

Upon analysis, it was determined that patients' Absolute Alpha Asymmetrywas correlated with mood score. Another means to calculate a bilateraldifference is a relative power asymmetry. Dividing the absolute alphapowers of the left and right channels by their respective total powersover the range of frequencies of interest (in this case, 0.5-20 Hz)normalizes the data for changes in overall EEG power levels andincreases the correlation with mood score. The normalized alpha power ofeach channel is called the Relative Alpha Power and the difference inthe left and right Relative Alpha Powers is the Relative AlphaAsymmetry. This parameter is calculated as the relative alpha power ofthe left hemisphere (i.e., calculated from EEG channel A₁-Fp_(Z)) minusthe relative alpha power of the right hemisphere (i.e., calculated fromEEG channel A₂-Fp_(Z)).

${{Relative\_ Alpha}{\_ Asymmetry}} = {\left( \frac{\sum\limits_{f = 8}^{12}{P(f)}_{A1\_ FPz}}{\sum\limits_{f = 0.5}^{20}{P(f)}_{A1\_ FPz}} \right) - \left( \frac{\sum\limits_{f = 8}^{12}{P(f)}_{A2\_ FPz}}{\sum\limits_{f = 0.5}^{20}{P(f)}_{A2\_ FPz}} \right)}$

The correlation of the inter-hemispheric difference in Relative AlphaPower with mood score is systematically positive, so that Relative AlphaPower of the left side of the head increases relative to the RelativeAlpha Power on the right side of the head as subjects feel better. ThePearson linear correlation (R) between Relative Alpha Asymmetry and thecorresponding mood score in MDD is 0.838 (p<0.001). In the combinedpopulation of MDD and OCD patients, the correlation of change inRelative Alpha Asymmetry with mood score is R=0.766 and is independentof disease etiology. A further finding is that the change in RelativeAlpha Asymmetry is inversely correlated with the change in Anxiety Scoreover the same period (R=−0.605, p<0.02); this relationship is alsoconsistent across individuals and etiologies (MDD and OCD). Again,although the preferred embodiment uses two channels of EEG data,alternate embodiments may include data from one or a plurality ofchannels. In addition, biological systems vary to some degree, sosomewhat different frequency ranges are likely to provide equivalentperformance. Similarly, other frequency ranges may be used.

An index is often specified to have the form of a linear equation. Thoseskilled in the art will readily recognize that other forms, such asnon-linear equations or neural networks, may be used as well. In thepreferred embodiment, the index has the general form

${Index} = {c_{0} + {\sum\limits_{i = 1}^{p}{c_{i}F_{i}}}}$where c₀ is a constant, {F_(i), i=1,2, . . . ,p} are a set of features,{c_(i), i=1,2, . . . ,p} are a set coefficients corresponding to thefeatures and p is the number of features.

An index to track the efficacy of neurostimulation to effect moodchanges may be calculated as:

Index_(Mood_1) = c₀ + c₁F₁$c_{0} = {\frac{100{\max\left( F_{1} \right)}}{\left( {{\max\left( F_{1} \right)} - {\min\left( F_{1} \right)}} \right)} = \frac{100}{\left( {1 - \frac{\min\left( F_{1} \right)}{\max\left( F_{1} \right)}} \right)}}$$c_{1} = {\frac{100 - c_{0}}{\min\left( F_{1} \right)} = \frac{- 100}{\left( {{\max\left( F_{1} \right)} - {\min\left( F_{1} \right)}} \right)}}$F₁ = Absolute_Alpha_Power

Here, c₀ and c₁ are defined such that the range of Index_(Mood) _(—) ₁will be between 0 (least efficacious state) and 100 (most efficaciousstate) for a feature F₁ (e.g., absolute alpha power) that decreases asefficacy increases (negative correlation). Based upon the database usedto derive this example, min(F₁)=122.9 and max(F₁)=191.9, resulting inc₀=278.12 and c₁=−1.45. The high correlation of alpha power with moodscore (R=−0.821, p=0.012) indicates that Index_(Mood) _(—) ₁ is asensitive measure of mood state.

Another index which quantifies the efficacy of neurostimulation toeffect mood changes may be calculated using the Relative Alpha Asymmetryas:

Index_(Mood_2) = c₀ + c₁F₁$c_{0} = {\frac{{- 100}{\min\left( F_{1} \right)}}{\left( {{\max\left( F_{1} \right)} - {\min\left( F_{1} \right)}} \right)} = \frac{100}{\left( {1 - \frac{\max\left( F_{1} \right)}{\min\left( F_{1} \right)}} \right)}}$$c_{1} = {\frac{100 - c_{0}}{\min\left( F_{1} \right)} = \frac{100}{\left( {{\max\left( F_{1} \right)} - {\min\left( F_{1} \right)}} \right)}}$F₁ = Relative_Alpha_Asymmetry

Again, c₀ and c₁ are defined such that the range of Index_(Mood) _(—) ₂will be between 0 (least efficacious state) and 100 (most efficaciousstate) for feature F₁ (e.g., Relative Alpha Asymmetry) that increases asefficacy increases (positive correlation). In the data set used toderive these results, min(F₁)=−0.048 and max(F₁)=0.068, resulting inC₀=41.379 and c₁=862.069. The high correlation of inter-hemisphericdifference in relative alpha power with mood score indicates thatIndex_(Mood) _(—) ₂ is a sensitive measure of mood state. Note that thedifferent form of the constants c0 and c1 in the two embodiments is dueto the sign of the correlation (positive vs. negative) between F₁ andmood score. It should be noted that in the case of a single feature, thevalues of c₀ and c₁ are simply scaling factors; if c₀=0 and c₁=1, thevalue of the index consisting of a single feature is simply the value offeature itself. Indices comprising a plurality of features may beimplemented as well, using the same general form as in the equationsabove. Although the preceding discussion is specific to indices derivedfrom inter-hemispheric EEG channels, features may calculated from one ora plurality of unilateral EEG channels as well as other montages ofbilateral EEG channels. Indices may also be constructed of bothunilateral and bilateral features in combination.

Features computed from different frequency bands may also be used. Forexample, in a preliminary development effort, it was determined that therelative power in the theta band (4-8 Hz) calculated from eitherhemisphere was negatively correlated with patients' mood scores.Therefore, an alternate index of mood score may be computed usingF₁=relative theta power, min(F₁)=0.005 and max(F₁)=0.310, yielding

Index_(Mood_3) = c₀ + c₁F₁$c_{0} = {\frac{100}{\left( {1 - \frac{\min\left( F_{1} \right)}{\max\left( F_{1} \right)}} \right)} = 101.639}$$c_{1} = {\frac{100 - c_{0}}{\min\left( F_{1} \right)} = {- 327.800}}$$F_{1} = {{{Relative\_ Theta}{\_ Power}} = \left( \frac{\sum\limits_{f = 4}^{8}{P(f)}_{A1\_ FPz}}{\sum\limits_{f = 0.5}^{20}{P(f)}_{A1\_ FPz}} \right)}$

Although this discussion is specific to indices derived from the powerspectral array, it is not limited to this method. Features may becalculated from various frequency regions of bispectral arrays (i.e.,bispectrum, complex triple product, real triple product, biphase andbicoherence, all for both auto and cross formulations), as well as crossspectral and coherence arrays. Other methods may be used to derivefeatures, such as medians, standard deviations and variances,percentiles, absolute power within a region bounded by specifiedfrequencies, relative power (absolute power as a percentage of totalpower within a region bounded by specified frequencies), neuralnetworks, fractal spectral analysis, measures derived from informationtheory such as entropy and complexity, and other statistical measuresknown to those skilled in the art. Features may also be derived fromvarious methods of time domain analysis such as pattern or templatematching. Features may also quantify the presence or absence of aspecific condition over a time period, or the degree to which a specificcondition is met over a specific time period (e.g., the percent of timein a recent period that the power in a specific frequency band of apower or bispectral array was less than a threshold value). Detectors ofspecific conditions or signal types may also be used as features or asan index having just two or more discrete states.

The computed indices or features are reflective of a patient'sneurological or psychological state. In the described embodiments, thevarious Index_(Mood) _(—) _(i) (i=1,2,3) are measures of the patient'smood, as quantified by the mood score. The invention may therefore beused to optimize a specific treatment modality by varying the treatmentparameters such that Index_(Mood) _(—) _(i) is increased to a maximumvalue. In the case of neurostimulation, the treatment parameters includethe amplitude, frequency, polarity and pulse width of the stimulatingsignal, as well as the subset of selected stimulating electrodes. Forother treatment modalities, the treatment parameters may include dosage(pharmacological treatment), stimulation voltage (ECT) and fieldstrength (TMS).

The system and method of the present invention monitors the treatmentefficacy of neurostimulation. Because the invention monitors the changein neural activity resulting from treatment, it is not dependent on aspecific treatment modality. Therefore, the invention may be used tomonitor the efficacy of other types of treatment as well, including butnot restricted to pharmacological treatment, electroconvulsive therapyand transcranial magnetic stimulation.

Testing Methodologies to Improve Sensitivity and Specificity

The sensitivity and specificity of the invention may be increasedthrough the use of differential testing methodologies. Differential testmethodologies use 2 or more consecutive assessments, and analyze thechange in the value of the test metric between the assessments as wellas the actual values at each of the assessments. The assessments aregenerally conducted under different conditions, such as sleep or underthe influence of a stressor such as a mental task; these are compared toa baseline assessment. Patients with dementia, depression, OCD and otherneurological disorders exhibit EEG responses different from that ofnormal subjects in a differential testing methodology. This descriptionwill describe several differential testing methodologies which may beused to increase the performance of the derived indices. Preferably, thetest metric is an index derived from the EEG spectral arrays, as well asother parameters, and will be denoted here as INDEX.

One differential test methodology takes advantage of the patient'svarying response when the stimulator is on and when it is off. Theelectrodes are first applied to the subject, who is instructed to sitquietly with eyes either open or closed. A baseline assessment isperformed with the neurostimulator 60 off in which the DAU 20 acquires asegment of EEG and transmits it to the DCU 30 for analysis. Generally,segments of several minutes are used to calculate the INDEX values. Afirst value of INDEX (denoted as INDEX_(stim) _(—) _(off)) is calculatedby the DCU 30 from the EEG segment. The neurostimulator 60 is thenturned on and a second segment of EEG is acquired by the DAU 20 andtransmitted to the DCU 30 for analysis. A second value of INDEX (denotedas INDEX_(stim) _(—) _(on)) is calculated by the DCU 30 from EEGacquired during the second assessment period. This later assessmentperiod may be when the neurostimulator 60 is turned on, or when it isturned off after having been on for a period of time. Examining theacquired data for artifact and either removing the detected artifact orexcluding the artifacted portion of the acquired data from analysis isan integral part of calculating an INDEX value. The difference betweenthe INDEX values obtained at these two assessment times, INDEX_(stim)_(—) _(on)−INDEX_(stim) _(—) _(off), constitutes an Index which may beused to quantify treatment efficacy. For example, the correlationbetween Relative Alpha Asymmetry and mood score may be improved bycomparing the change in Relative Alpha Asymmetry from baseline(stimulator off) to subsequent periods when the stimulator was either onor was off after having been on. The change in Relative Alpha Asymmetryin MDD is strongly correlated with the change in mood score over thesame period (R=0.872, p<0.001). This relationship is independent ofstimulation mode (bipolar stimulation, monopolar stimulation, andstimulator off). This differential methodology could be expanded bycomparing INDEX values with the neurostimulator at different controlsettings, e.g., different stimulation signal frequencies (repetitionrates), pulse widths, pulse amplitudes and duty cycles, lead selections,and stimulator signal polarities.

Another test methodology calculates the difference between a first valueof INDEX calculated from EEG acquired with the subject's eyes open and asecond value of INDEX calculated from EEG acquired with the subject'seyes closed. The neurostimulator 60 may be either on or off during anyof the assessments. The electrodes 15 are first applied to the subject,who is instructed to sit quietly with eyes open. A segment of EEG isacquired by the DAU 20 and transmitted to the DCU 30 for analysis.Generally, segments of several minutes are used to calculate the INDEXvalues. The subject is next directed to sit quietly with eyes closed,and a second segment of EEG is acquired by the DAU 20 and transmitted tothe DCU 30 for analysis. The DCU 30 calculates INDEX values for both thefirst and second periods of acquired data, referred to as INDEX_(eyes)_(—) _(open) and INDEX_(eyes) _(—) _(closed). Examining the acquireddata for artifact and either removing the detected artifact or excludingthe artifacted portion of the acquired data from analysis is an integralpart of calculating an INDEX value. The numerical difference betweenINDEX_(eyes) _(—) _(open) and INDEX_(eyes) _(—) _(closed) constitutes anIndex which may be used to quantify treatment efficacy.

A third differential test methodology calculates the difference betweena first value of INDEX calculated from EEG acquired with the subject ina relaxed state and a second value of INDEX calculated from EEG acquiredwhile the subject is performing a mental calculation task. Theneurostimulator 60 may be either on or off during any of theassessments. The subject may be directed to keep his/her eyes openduring both recording periods. Alternatively, the subject may bedirected to close their eyes during both recording periods, though thismay restrict the mental calculation tasks that may be chosen. The mentalcalculation task may be any simple task or set of tasks chosen toprovide adequate difficulty yet universal enough to not require specialtraining or a level of education not universal in the population to betested. Two example tasks are mental addition and subtraction ofnumbers, as would be required in balancing a check book or countingbackward from one hundred by threes, and the calculation of the numberof days between two dates. The electrodes 15 are first applied to thesubject, who is instructed to sit quietly. A segment of EEG is acquiredby the DAU 20 and transmitted to the DCU 30 for analysis. Again,segments of several minutes are used to calculate the INDEX values. Thesubject is next given instruction in the mental task and then asked tocomplete it. A second segment of EEG is acquired by the DAU 20 duringthe period of mental calculation. The acquired data is then transmittedto the DCU 30 for analysis. The DCU 30 calculates INDEX values for boththe first and second periods of acquired data, referred to asINDEX_(baseline) and INDEX_(task). The numerical difference betweenINDEX_(task) and INDEX_(baseline) constitutes an Index which may be usedto quantify treatment efficacy.

Automated Adjustment of Neurostimulator Parameters to Obtain MaximalTreatment Efficacy

A baseline measure of EEG state can be assessed by calculation of theIndex when the neurostimulator is disabled. This value may be comparedto the Index calculated at various neurostimulator parameters(settings). The greatest treatment efficacy and therefore the optimalneurostimulator parameters would correspond to those which maximized thedifference between the corresponding Index values and the baseline Indexvalue. As the Index value is a univariate measure of neurostimulatorefficacy, a control signal can be supplied from the DCU 30 to theneurostimulator 60. This control signal could be used to control thevarious neurostimulator parameters. Various combinations ofneurostimulator settings could be automatically selected by the DCU 30and an Index value calculated for each setting. The optimalneurostimulator parameters would be determined to be those at which theIndex is the greatest difference from a baseline (neurostimulator off)value of the Index. The DCU 30 would then command the neurostimulator toconfigure itself using the parameters determined to be optimum.

In general, neurostimulators have 4 or more parameters that may beadjusted, often in a continuous fashion. Therefore, the number ofparameter combinations is very large. Different strategies may beemployed to reduce the number of parameter combinations examined whilestill finding a local maximum value of the index (assuming that maximumtreatment efficacy is obtained with a maximal INDEX value). Forinstance, all parameters may be initially set at a nominal value, thenone parameter is adjusted over its range. The DCU 30 will record theparameter value that generates the maximum INDEX difference frombaseline. This process will be repeated for all parameters. At the endof the process, the neurostimulator 60 will be configured by the DCU 30setting each parameter to the optimum setting. In an alternateembodiment of the index, settings that produce local minimum value ofthe index may be desired. The invention described here usesneurostimulation as a treatment. However, the same invention may beapplied to other treatments, such as administration of pharmacologicalagents, electroconvulsive therapy and transcranial magnetic stimulation.In the case of the former, the agent, the dose or the dosing regimen maybe varied; in the latter two, the parameters of the shock may be varied.

While the foregoing invention has been described with reference to itspreferred embodiments, various alterations and modifications will occurto those skilled in the art. All such alterations and modifications areintended to fall within the scope of the appended claims.

1. A system for assessing the efficacy of treatment of a neurologicaldisorder comprising: at least two electrodes for acquiringelectrophysiological signals from the body suffering from a neurologicaldisorder which is one or more of depression, major depressive disorder,obsessive compulsive disorder, dementia, mood disorders and anxietydisorders, wherein one of said at least two electrodes for acquiringelectrophysiological signals from the body is positioned at electrodeposition Fp_(z); a processor for calculating from saidelectrophysiological signals at least one feature relating to theefficacy of said treatment without referencing said at least one featureto a normative data set, said at least one feature being a measure of aself-reported mood or anxiety score.
 2. The system for assessing theefficacy of treatment of a neurological disorder of claim 1 wherein saidtreatment is neurostimulation.
 3. The system for assessing the efficacyof treatment of a neurological disorder of claim 2 wherein saidneurostimulation is deep brain stimulation.
 4. The system for assessingthe efficacy of treatment of a neurological disorder of claim 2 whereinsaid neurostimulation is vagus nerve stimulation.
 5. The system forassessing the efficacy of treatment of a neurological disorder of claim1 wherein said treatment is the administration of a pharmacologicalagent.
 6. The system for assessing the efficacy of treatment of aneurological disorder of claim 1 wherein said treatment iselectroconvulsive therapy.
 7. The system for assessing the efficacy oftreatment of a neurological disorder of claim 1 wherein said treatmentis transcranial magnetic stimulation.
 8. The system for assessing theefficacy of treatment of a neurological disorder of claim 1 wherein saidprocessor calculates at least two features and combines said at leasttwo features into an index.
 9. The system for assessing the efficacy oftreatment of a neurological disorder of claim 1 wherein said processorcalculates at least one feature from a spectral array.
 10. The systemfor assessing the efficacy of treatment of a neurological disorder ofclaim 8 wherein said processor calculates at least one feature from apower spectral array.
 11. The system for assessing the efficacy oftreatment of a neurological disorder of claim 8 wherein said processorcalculates at least one feature from a bispectral array.
 12. The systemfor assessing the efficacy of treatment of a neurological disorder ofclaim 1 wherein said at least one feature is a time domain feature. 13.The system for assessing the efficacy of treatment of a neurologicaldisorder of claim 1 wherein said at least two electrodes are placed in abilateral montage.
 14. The system for assessing the efficacy oftreatment of a neurological disorder of claim 1 wherein said at leasttwo electrodes are placed in an unilateral montage.
 15. The system forassessing the efficacy of treatment of a neurological disorder of claim1 wherein said feature is the interhemispheric difference in a metriccalculated from each electrophysiological signal.
 16. The system forassessing the efficacy of treatment of a neurological disorder of claim15 wherein said metric is a spectral feature.
 17. The system forassessing the efficacy of treatment of a neurological disorder of claim15 wherein said metric is a time domain feature.
 18. A system forassessing the efficacy of treatment of a neurological disordercomprising: at least two electrodes for acquiring electrophysiologicalsignals from a body suffering from a neurological disorder which is oneor more of depression, major depressive disorder, obsessive compulsivedisorder, dementia, mood disorders and anxiety disorders, wherein one ofsaid at least two electrodes for acquiring electrophysiological signalsfrom the body is positioned at electrode position Fp_(z); dataacquisition circuitry for acquiring from said electrodes a firstelectrophysiological signal representing a baseline condition and asecond electrophysiological signal representing a subsequent condition;a processor for calculating from said electrophysiological signalsreceived from the data acquisition circuitry: (a) at least one featurerelating to the patient state during the baseline condition, withoutreferencing said at least one feature relating to the patient stateduring the baseline condition to a normative data set, said at least onefeature relating to the patient state during the baseline conditionbeing a measure of a self-reported mood or anxiety score; (b) at leastone feature relating to the patient state during the subsequentcondition, without referencing said at least one feature relating to thepatient state during the subsequent condition to a normative data set,said at least one feature relating to the patient state during thesubsequent condition being a measure of a self-reported mood or anxietyscore; and (c) the difference between said features relating to thebaseline and subsequent conditions, such that said difference relates tothe efficacy of said treatment.
 19. A system for optimizing the efficacyof treatment of a neurological disorder comprising: at least twoelectrodes for acquiring electrophysiological signals from a bodysuffering from a neurological disorder which is one or more ofdepression, major depressive disorder, obsessive compulsive disorder,dementia, mood disorders and anxiety disorders, wherein one of said atleast two electrodes for acquiring electrophysiological signals from thebody is positioned at electrode position Fp_(z); processor forcalculating from said electrophysiological signals at least one featurerelating to the efficacy of said treatment without referencing said atleast one feature to a normative data set, said feature being a measureof a self-reported mood or anxiety score; data acquisition circuitry foracquiring said electrophysiological signals from said electrodes andconverting said electrophysiological signals to a form usable by saidprocessor; a processor for varying the treatment parameters of aneurostimulator in order to maximize the calculated treatment efficacy.20. A method of assessing the efficacy of treatment of a neurologicaldisorder comprising the steps of: acquiring electrophysiological signalsfrom a body suffering from a neurological disorder which is one or moreof depression, major depressive disorder, obsessive compulsive disorder,dementia, mood disorders and anxiety disorders, said signals beingacquired through electrodes placed on the body, wherein one of said atleast two electrodes for acquiring electrophysiological signals from thebody is positioned at electrode position Fp_(z); calculating from saidelectrophysiological signals at least one feature relating to theefficacy of said treatment without referencing said at least one featureto a normative data set, said feature being a measure of a self-reportedmood or anxiety score.
 21. The method of assessing the efficacy oftreatment of a neurological disorder of claim 20 wherein said treatmentis neurostimulation.
 22. The method of assessing the efficacy oftreatment of a neurological disorder of claim 21 wherein saidneurostimulation is deep brain stimulation.
 23. The method of assessingthe efficacy of treatment of a neurological disorder of claim 21 whereinsaid neurostimulation is vagus nerve stimulation.
 24. The method ofassessing the efficacy of treatment of a neurological disorder of claim20 wherein said treatment is the administration of a pharmacologicalagent.
 25. The method of assessing the efficacy of treatment of aneurological disorder of claim 20 wherein said treatment iselectroconvulsive therapy.
 26. The method of assessing the efficacy oftreatment of a neurological disorder of claim 20 wherein said treatmentis transcranial magnetic stimulation.
 27. The method of assessing theefficacy of treatment of a neurological disorder of claim 20 furthercomprising the step of combining said features into an index.
 28. Themethod of assessing the efficacy of treatment of a neurological disorderof claim 20 wherein said at least one feature is calculated from aspectral array.
 29. The method of assessing the efficacy of treatment ofa neurological disorder of claim 27 wherein said at least one feature iscalculated from a power spectral array.
 30. The method of assessing theefficacy of treatment of a neurological disorder of claim 27 whereinsaid at least one feature is calculated from a bispectral array.
 31. Themethod of assessing the efficacy of treatment of a neurological disorderof claim 20 wherein said at least one feature is a time domain feature.32. The method of assessing the efficacy of treatment of a neurologicaldisorder of claim 20 wherein said at least two electrodes are placed ina bilateral montage.
 33. The method of assessing the efficacy oftreatment of a neurological disorder of claim 20 wherein said at leasttwo electrodes are placed in an unilateral montage.
 34. The method ofassessing the efficacy of treatment of a neurological disorder of claim20 wherein said feature is the interhemispheric difference in a metriccalculated from each electrophysiological signal.
 35. The method ofassessing the efficacy of treatment of a neurological disorder of claim34 wherein said metric is a spectral feature.
 36. The method ofassessing the efficacy of treatment of a neurological disorder of claim34 wherein said metric is a time domain feature.
 37. A method ofassessing the efficacy of treatment of a neurological disordercomprising: positioning a least two electrodes on a body being treatedfor a neurological disorder which is one or more of depression, majordepressive disorder, obsessive compulsive disorder, dementia, mooddisorders and anxiety disorders, wherein one of said at least twoelectrodes for acquiring electrophysiological signals from the body ispositioned at electrode position Fp_(z); acquiring a firstelectrophysiological signal from the body at a baseline condition;acquiring a second electrophysiological signal from the body during asubsequent condition; calculating at least one feature relating to thepatient state during the baseline condition without referencing said atleast one feature relating to the patient state during the baselinecondition to a normative data set, said at least one feature relating tothe patient state during the baseline condition being a measure of aself-reported mood or anxiety score; calculating at least one featurerelating to the patient state during the subsequent condition withoutreferencing said at least one feature relating to the patient stateduring the subsequent condition to a normative data set, said at leastone feature relating to the patient state during the subsequentcondition being a measure of a self-reported mood or anxiety score;calculating at the difference between the features calculated during thebaseline and subsequent conditions, such that the difference relates tothe efficacy of said treatment.
 38. A method of optimizing the efficacyof treatment of a neurological disorder comprising: positioning at leasttwo electrodes on a body being treated for a neurological disorder whichis one or more of depression, major depressive disorder, obsessivecompulsive disorder, dementia, mood disorders and anxiety disorders,wherein one of said at least two electrodes for acquiringelectrophysiological signals from the body is positioned at electrodeposition Fp_(z); acquiring electrophysiological signals from the body;calculating at least one feature relating to the efficacy of saidtreatment without referencing said at least one feature to a normativedata set, said at least one feature being a measure of a self-reportedmood or anxiety score; varying treatment parameters in order to maximizethe calculated treatment efficacy.